| --- |
| pretty_name: SPEC |
| task_categories: |
| - image-to-text |
| - text-to-image |
| - image-classification |
| tags: |
| - image |
| - text |
| language: |
| - en |
| license: apache-2.0 |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # [CVPR 2024] SPEC Benchmark: Evaluating VLMs in Fine-grained and Compositional Understanding |
| introduced in the CVPR 2024 paper [Synthesize, Diagnose, and Optimize: Towards Fine-Grained Vision-Language Understanding](https://huggingface.co/papers/2312.00081) |
|
|
| [**Code**](https://github.com/wjpoom/SPEC) | [**🤗 Paper**](https://huggingface.co/papers/2312.00081) | [**📖 arXiv**](https://arxiv.org/abs/2312.00081) |
|
|
| To evaluate the understanding capability of visual-language models on fine-grained concepts, we propose a new benchmark, SPEC, |
| which consists of six distinct subsets, distributed across the dimensions of **S**ize, **P**osition, **E**xistence, and **C**ount. |
| Each test case consists of an image candidate set, which differs only in certain visual concepts, and a text candidate set, |
| which differs only in the corresponding language concept. |
| <p align="center"> |
| <img src="https://cdn-uploads.huggingface.co/production/uploads/649bce4f200e2dff194d9883/sE65-zVjY_HXUT4-eaqZ9.png" width="90%"/> |
| <be> |
| </p> |
|
|
| ## 🔧 Usage |
| ### install |
| ``` shell |
| git clone https://github.com/wjpoom/SPEC.git |
| cd SPEC/ |
| pip install -e . |
| ``` |
| ### prepare data |
| * run the following code in Python shell, replace `/path/to/save/data` with a specified dir to store the data. |
| ```python |
| import zipfile |
| import os |
| from huggingface_hub import hf_hub_download |
| |
| data_root = '/path/to/save/data' |
| hf_hub_download(repo_id='wjpoom/SPEC', repo_type='dataset', filename='data.zip', local_dir=data_root) |
| |
| with zipfile.ZipFile(os.path.join(data_root, 'data.zip'), 'r') as zip_ref: |
| zip_ref.extractall(os.path.join(data_root)) |
| |
| os.remove(os.path.join(data_root, 'data.zip')) |
| ``` |
| ### explore the dataset |
| * We provide a 📓notebook that enables you to visually explore the test samples in the SPEC dataset. |
| * Run this notebook either [locally](https://github.com/wjpoom/SPEC/blob/main/notebooks/explore_spec_local.ipynb) or online using [Colab](https://colab.research.google.com/github/wjpoom/SPEC/blob/main/notebooks/explore_spec_colab.ipynb). |
|
|
| ### reproduce the results |
| * In our paper, we evaluated four popular VLMs using our SPEC dataset, namely: CLIP, BLIP, FLAVA and CoCa. |
| * To reproduce the results with these VLMs, you can run [this script](https://github.com/wjpoom/SPEC/blob/main/spec/run_eval.sh). |
| * You can also reproduce with this [local notebook](https://github.com/wjpoom/SPEC/blob/main/notebooks/evaluate_example_local.ipynb) or the online [Colab notebook](https://colab.research.google.com/github/wjpoom/SPEC/blob/main/notebooks/evaluate_example_colab.ipynb). |
|
|
| ### evaluate custom VLMs |
| * If you want to evaluate your custom model on SPEC, you can follow the instructions in [this document](https://github.com/wjpoom/SPEC/blob/main/docs/evaluate_custom_model.md). |
|
|
| * ## ✒️ Citation |
| If you use our code or data in this repo or find our work helpful, please consider giving a citation: |
|
|
| ``` |
| @inproceedings{spec2024, |
| title={Synthesize Diagnose and Optimize: Towards Fine-Grained Vision-Language Understanding}, |
| author={Peng, Wujian and Xie, Sicheng and You, Zuyao and Lan, Shiyi and Wu, Zuxuan}, |
| booktitle={CVPR}, |
| year={2024} |
| } |
| ``` |
